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Transcriptomic Profiling of Subcutaneous Adipose Tissue in Relation to Bariatric Surgery: A Retrospective, Pooled Re-analysis.


ABSTRACT:

Background

Bariatric surgery is the most effective intervention for weight loss possibly through modulating subcutaneous adipose tissue (SAT) molecular programs. The post-operative molecular and biological impacts, including gene expression, deserve in-depth investigation especially given the small sample sizes in the literature.

Methods

Five existing datasets (n=237 SATs) were re-processed and corrected for batch-to-batch variation. Unsupervised approaches and robust linear mixed effect model were used to compare gene expression post- (n=126) to pre-operation (n=111).

Results

Post-operative SATs showed distinct global gene expression. Forty-four and 395 genes were over- and under-expressed post-operation (all Bonferroni P<0.05). The under-expressed genes significantly enriched for 21 biological processes/pathways (all Bonferroni P<0.05), 17 (76.2%) and two (9.5%) directly involved in immunity and amino/proteo-glycan metabolism, respectively.

Conclusion

Post-operative SATs might adopt distinct transcriptomic landscapes and undergo a reduction in immune-related processes and amino/proteo-glycan metabolism.

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10088552 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Transcriptomic Profiling of Subcutaneous Adipose Tissue in Relation to Bariatric Surgery: A Retrospective, Pooled Re-analysis.

Chen Youdinghuan Y  

Journal of obesity & metabolic syndrome 20230201 1


<h4>Background</h4>Bariatric surgery is the most effective intervention for weight loss possibly through modulating subcutaneous adipose tissue (SAT) molecular programs. The post-operative molecular and biological impacts, including gene expression, deserve in-depth investigation especially given the small sample sizes in the literature.<h4>Methods</h4>Five existing datasets (n=237 SATs) were re-processed and corrected for batch-to-batch variation. Unsupervised approaches and robust linear mixed  ...[more]

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